Devices for determining the distance to objects are known.
Current light (or laser) range and detect (LIDAR) devices and/or 3D map/depth devices are typically limited to a single application for which they are optimized. For example, in some devices multiple cameras or camera arrays provide images may be used to determine the range. Computational camera applications may compare features within these images and using the knowledge of intrinsic and extrinsic parameters associated with the cameras or camera arrays determine the distance from the device. Computational camera applications thus can create 3D images with associated 3D depth maps. The applications can, for example, employ such techniques as foreground-background separation, 3D scanning, and 3D modeling. These 3D depth maps or models may then be employed in Augmented Reality (AR), Virtual Reality (VR) and even logistic applications.
Accuracy, speed and consistency of the 3D/depth computation are important for the key use cases such as 3D scanning and modeling. For instance, the 3D scanning device should generate consistent 3D models, which can be used by the logistics company for storage space estimation. Errors in the 3D models can for example lead to incorrect space estimation.
However limitations in cameras, algorithms and device production prevent effective correction of all errors, motions and variations. Furthermore, these issues are typically worse in mobile devices because of the limited computation power, battery capacity and movement of the device during capture.